import os
import time
import warnings
# ignore warning
warnings.filterwarnings("ignore")

%load_ext autoreload
%autoreload 2

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Set pandas display options to maximize output visibility
pd.set_option("display.max_columns", None)
pd.set_option("display.max_colwidth", None)
pd.set_option("display.width", 0)
pd.set_option("display.expand_frame_repr", False)

# Import dataset
datasource = "dataset/employee_data.csv"
employee_df = pd.read_csv(datasource,encoding='windows-1252')

print("Dataframe Info:\n")
employee_df.info()

Dataframe Info:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1470 entries, 0 to 1469
Data columns (total 35 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   EmployeeId                1470 non-null   int64  
 1   Age                       1470 non-null   int64  
 2   Attrition                 1058 non-null   float64
 3   BusinessTravel            1470 non-null   object 
 4   DailyRate                 1470 non-null   int64  
 5   Department                1470 non-null   object 
 6   DistanceFromHome          1470 non-null   int64  
 7   Education                 1470 non-null   int64  
 8   EducationField            1470 non-null   object 
 9   EmployeeCount             1470 non-null   int64  
 10  EnvironmentSatisfaction   1470 non-null   int64  
 11  Gender                    1470 non-null   object 
 12  HourlyRate                1470 non-null   int64  
 13  JobInvolvement            1470 non-null   int64  
 14  JobLevel                  1470 non-null   int64  
 15  JobRole                   1470 non-null   object 
 16  JobSatisfaction           1470 non-null   int64  
 17  MaritalStatus             1470 non-null   object 
 18  MonthlyIncome             1470 non-null   int64  
 19  MonthlyRate               1470 non-null   int64  
 20  NumCompaniesWorked        1470 non-null   int64  
 21  Over18                    1470 non-null   object 
 22  OverTime                  1470 non-null   object 
 23  PercentSalaryHike         1470 non-null   int64  
 24  PerformanceRating         1470 non-null   int64  
 25  RelationshipSatisfaction  1470 non-null   int64  
 26  StandardHours             1470 non-null   int64  
 27  StockOptionLevel          1470 non-null   int64  
 28  TotalWorkingYears         1470 non-null   int64  
 29  TrainingTimesLastYear     1470 non-null   int64  
 30  WorkLifeBalance           1470 non-null   int64  
 31  YearsAtCompany            1470 non-null   int64  
 32  YearsInCurrentRole        1470 non-null   int64  
 33  YearsSinceLastPromotion   1470 non-null   int64  
 34  YearsWithCurrManager      1470 non-null   int64  
dtypes: float64(1), int64(26), object(8)
memory usage: 402.1+ KB

employee_df.sample(10)

# Displaying the number of unique values in each column
employee_df.nunique().to_frame()

# Unique values from the 'Attrition' column
employee_df['Attrition'].unique()

array([nan,  1.,  0.])

# Listing type data kolom

employee_col_numeric_int = [
    'Age', 'DailyRate', 'DistanceFromHome',
    'EmployeeCount', 'HourlyRate',
    'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',
    'PercentSalaryHike', 'StandardHours',
    'TotalWorkingYears', 'TrainingTimesLastYear',
    'YearsAtCompany', 'YearsInCurrentRole',
    'YearsSinceLastPromotion', 'YearsWithCurrManager'
]
employee_col_numeric_int = pd.Index(employee_col_numeric_int)

employee_col_categorical = [
    'BusinessTravel', 'Department', 'Education', 'EducationField',
    'EnvironmentSatisfaction', 'Gender', 'JobInvolvement', 'JobLevel',
    'JobRole', 'JobSatisfaction', 'MaritalStatus', 'Over18', 'OverTime',
    'PerformanceRating', 'RelationshipSatisfaction', 'StockOptionLevel',
    'WorkLifeBalance'
]
employee_col_categorical = pd.Index(employee_col_categorical)

employee_col_object = ['EmployeeId']
employee_col_object = pd.Index(employee_col_object)

# Convert to integer
employee_df[employee_col_numeric_int] = employee_df[employee_col_numeric_int].astype('int64')

# Conversion to category
employee_df[employee_col_categorical] = employee_df[employee_col_categorical].astype('category')

# Convert object columns (outside of other categories)
employee_df['EmployeeId'] = employee_df['EmployeeId'].astype('object')

employee_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1470 entries, 0 to 1469
Data columns (total 35 columns):
 #   Column                    Non-Null Count  Dtype   
---  ------                    --------------  -----   
 0   EmployeeId                1470 non-null   object  
 1   Age                       1470 non-null   int64   
 2   Attrition                 1058 non-null   float64 
 3   BusinessTravel            1470 non-null   category
 4   DailyRate                 1470 non-null   int64   
 5   Department                1470 non-null   category
 6   DistanceFromHome          1470 non-null   int64   
 7   Education                 1470 non-null   category
 8   EducationField            1470 non-null   category
 9   EmployeeCount             1470 non-null   int64   
 10  EnvironmentSatisfaction   1470 non-null   category
 11  Gender                    1470 non-null   category
 12  HourlyRate                1470 non-null   int64   
 13  JobInvolvement            1470 non-null   category
 14  JobLevel                  1470 non-null   category
 15  JobRole                   1470 non-null   category
 16  JobSatisfaction           1470 non-null   category
 17  MaritalStatus             1470 non-null   category
 18  MonthlyIncome             1470 non-null   int64   
 19  MonthlyRate               1470 non-null   int64   
 20  NumCompaniesWorked        1470 non-null   int64   
 21  Over18                    1470 non-null   category
 22  OverTime                  1470 non-null   category
 23  PercentSalaryHike         1470 non-null   int64   
 24  PerformanceRating         1470 non-null   category
 25  RelationshipSatisfaction  1470 non-null   category
 26  StandardHours             1470 non-null   int64   
 27  StockOptionLevel          1470 non-null   category
 28  TotalWorkingYears         1470 non-null   int64   
 29  TrainingTimesLastYear     1470 non-null   int64   
 30  WorkLifeBalance           1470 non-null   category
 31  YearsAtCompany            1470 non-null   int64   
 32  YearsInCurrentRole        1470 non-null   int64   
 33  YearsSinceLastPromotion   1470 non-null   int64   
 34  YearsWithCurrManager      1470 non-null   int64   
dtypes: category(17), float64(1), int64(16), object(1)
memory usage: 234.3+ KB

# Removing Zero-Variance Feature
try:
    print("Column with only one unique value:")
    const_cols = [col for col in employee_df.columns if employee_df[col].nunique() == 1]
    print(const_cols)

    employee_df = employee_df.drop(columns=const_cols)

    print("Columns with only one unique value have been removed.")

except KeyError as e:
    print(f"Column not found when dropping: {e}")
except Exception as e:
    print(f"Terjadi error: {e}")
finally:
    print("Zero-Variance column cleaning is complete.")

Column with only one unique value:
['EmployeeCount', 'Over18', 'StandardHours']
Columns with only one unique value have been removed.
Zero-Variance column cleaning is complete.

employee_df.isnull().sum().to_frame()

# Separate rows with NaN values in the 'Attrition' column.
employee_attrition_nan = employee_df[employee_df['Attrition'].isna()]

# Save to CSV file will be used for the prediction file.
employee_attrition_nan.to_csv('saved/employee_attrition_nan.csv', index=False)

# Drop rows with NaN values in the 'Attrition' column to create a clean dataset.
employee_df_clean = employee_df.dropna(subset=['Attrition'])

# Convert the 'Attrition' column to integer type
employee_df_clean['Attrition'] = employee_df_clean['Attrition'].astype(int)

# Show the number of null values after cleaning
print("\nNumber of null values after being cleaned:\n")
employee_df_clean.isnull().sum().to_frame()

Number of null values after being cleaned:

# Identify outliers using the Interquartile Range (IQR) method.
from scripts.runPlot_DetectOutliersIQR import plot_detect_outliers_iqr

print("\nOutliers detected using IQR method:\n")
ignore_outlier_cols = ['Attrition']
plot_detect_outliers_iqr(df=employee_df_clean, ignore_cols=ignore_outlier_cols)

Outliers detected using IQR method:

[INFO] No outliers detected in 'Age'.
[INFO] No outliers detected in 'DailyRate'.
[INFO] No outliers detected in 'DistanceFromHome'.
[INFO] No outliers detected in 'HourlyRate'.
[INFO] No outliers detected in 'MonthlyRate'.
[INFO] No outliers detected in 'PercentSalaryHike'.

[OUTLIER] 'MonthlyIncome' — 64 values detected
   Q1 = 2900.25, Median = 4903.50, Q3 = 8736.50
   Lower bound = -5854.12, Upper bound = 17490.88
   Values:
  { 17584: 1x, 17639: 1x, 17650: 1x, 17665: 1x, 17779: 1x, 17856: 1x, 17861: 2x,
  17924: 1x, 18041: 1x, 18061: 1x, 18172: 1x, 18200: 1x, 18213: 1x, 18265: 1x,
  18300: 1x, 18303: 1x, 18430: 1x, 18606: 1x, 18665: 1x, 18711: 1x, 18722: 1x,
  18740: 1x, 18789: 1x, 18824: 1x, 18844: 1x, 18947: 1x, 19033: 1x, 19045: 1x,
  19068: 1x, 19081: 1x, 19094: 1x, 19141: 1x, 19144: 1x, 19187: 1x, 19189: 1x,
  19190: 1x, 19197: 1x, 19202: 1x, 19232: 1x, 19237: 1x, 19246: 1x, 19272: 1x,
  19406: 1x, 19419: 1x, 19436: 1x, 19502: 1x, 19513: 1x, 19517: 1x, 19537: 1x,
  19545: 1x, 19566: 1x, 19626: 1x, 19627: 1x, 19701: 1x, 19717: 1x, 19740: 1x,
  19845: 1x, 19847: 1x, 19859: 1x, 19926: 1x, 19943: 1x, 19973: 1x, 19999: 1x }

[OUTLIER] 'NumCompaniesWorked' — 43 values detected
   Q1 = 1.00, Median = 2.00, Q3 = 4.00
   Lower bound = -3.50, Upper bound = 8.50
   Values:
  { 9: 43x }

[OUTLIER] 'TotalWorkingYears' — 29 values detected
   Q1 = 6.00, Median = 10.00, Q3 = 16.00
   Lower bound = -9.00, Upper bound = 31.00
   Values:
  { 32: 7x, 33: 7x, 34: 3x, 35: 2x, 36: 4x, 37: 3x, 38: 1x, 40: 2x }

[OUTLIER] 'TrainingTimesLastYear' — 174 values detected
   Q1 = 2.00, Median = 3.00, Q3 = 3.00
   Lower bound = 0.50, Upper bound = 4.50
   Values:
  { 0: 43x, 5: 87x, 6: 44x }

[OUTLIER] 'YearsAtCompany' — 79 values detected
   Q1 = 3.00, Median = 5.00, Q3 = 9.00
   Lower bound = -6.00, Upper bound = 18.00
   Values:
  { 19: 9x, 20: 20x, 21: 11x, 22: 9x, 24: 6x, 25: 4x, 26: 3x, 27: 2x, 29: 2x, 31:
  3x, 32: 2x, 33: 4x, 34: 1x, 36: 1x, 37: 1x, 40: 1x }

[OUTLIER] 'YearsInCurrentRole' — 15 values detected
   Q1 = 2.00, Median = 3.00, Q3 = 7.00
   Lower bound = -5.50, Upper bound = 14.50
   Values:
  { 15: 5x, 16: 6x, 17: 2x, 18: 2x }

[OUTLIER] 'YearsSinceLastPromotion' — 80 values detected
   Q1 = 0.00, Median = 1.00, Q3 = 3.00
   Lower bound = -4.50, Upper bound = 7.50
   Values:
  { 8: 11x, 9: 14x, 10: 5x, 11: 21x, 12: 6x, 13: 7x, 14: 3x, 15: 13x }

[OUTLIER] 'YearsWithCurrManager' — 12 values detected
   Q1 = 2.00, Median = 3.00, Q3 = 7.00
   Lower bound = -5.50, Upper bound = 14.50
   Values:
  { 15: 5x, 16: 1x, 17: 6x }

employee_plot = employee_df_clean.copy()

# list of numeric columns
numeric_cols = employee_df_clean.select_dtypes(include=['number']).columns.tolist()
numeric_cols = pd.Index(numeric_cols)
print(numeric_cols)

# list of category columns
categorical_cols = employee_df_clean.select_dtypes(include=['object', 'category', 'bool']).columns.tolist()
categorical_cols.remove('EmployeeId')
categorical_cols = pd.Index(categorical_cols)
print(categorical_cols)

label_map = {
    0: 'Active',
    1: 'Resigned'
}

education_map = {
    "1": "1-Below College",
    "2": "2-College",
    "3": "3-Bachelor",
    "4": "4-Master",
    "5": "5-Doctor"
}

env_satisfaction_map = {
    "1": "1-Low",
    "2": "2-Medium",
    "3": "3-High",
    "4": "4-Very High"
}

job_involvement_map = {
    "1": "1-Low",
    "2": "2-Medium",
    "3": "3-High",
    "4": "4-Very High"
}

job_level_map = {
    "1": "1-Entry Level",
    "2": "2-Junior",
    "3": "3-Mid Level",
    "4": "4-Senior",
    "5": "5-Executive/Top Level"
}

job_satisfaction_map = {
    "1": "1-Low",
    "2": "2-Medium",
    "3": "3-High",
    "4": "4-Very High"
}

performance_rating_map = {
    "1": "1-Low",
    "2": "2-Good",
    "3": "3-Excellent",
    "4": "4-Outstanding"
}

relationship_satisfaction_map = {
    "1": "1-Low",
    "2": "2-Medium",
    "3": "3-High",
    "4": "4-Very High"
}

stock_option_level_map = {
    "0": "0-None",
    "1": "1-Low",
    "2": "2-Medium",
    "3": "3-High"
}

work_life_balance_map = {
    "1": "1-Low",
    "2": "2-Good",
    "3": "3-Excellent",
    "4": "4-Outstanding"
}

employee_plot["Education"] = employee_plot["Education"].astype(str).map(education_map)
employee_plot["EnvironmentSatisfaction"] = employee_plot["EnvironmentSatisfaction"].astype(str).map(env_satisfaction_map)
employee_plot["JobLevel"] = employee_plot["JobLevel"].astype(str).map(job_level_map)
employee_plot["JobInvolvement"] = employee_plot["JobInvolvement"].astype(str).map(job_involvement_map)
employee_plot["JobSatisfaction"] = employee_plot["JobSatisfaction"].astype(str).map(job_satisfaction_map)
employee_plot["PerformanceRating"] = employee_plot["PerformanceRating"].astype(str).map(performance_rating_map)
employee_plot["RelationshipSatisfaction"] = employee_plot["RelationshipSatisfaction"].astype(str).map(relationship_satisfaction_map)
employee_plot["StockOptionLevel"] = employee_plot["StockOptionLevel"].astype(str).map(stock_option_level_map)
employee_plot["WorkLifeBalance"] = employee_plot["WorkLifeBalance"].astype(str).map(work_life_balance_map)

employee_plot.info()

Index(['Age', 'Attrition', 'DailyRate', 'DistanceFromHome', 'HourlyRate',
       'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',
       'PercentSalaryHike', 'TotalWorkingYears', 'TrainingTimesLastYear',
       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',
       'YearsWithCurrManager'],
      dtype='object')
Index(['BusinessTravel', 'Department', 'Education', 'EducationField',
       'EnvironmentSatisfaction', 'Gender', 'JobInvolvement', 'JobLevel',
       'JobRole', 'JobSatisfaction', 'MaritalStatus', 'OverTime',
       'PerformanceRating', 'RelationshipSatisfaction', 'StockOptionLevel',
       'WorkLifeBalance'],
      dtype='object')
<class 'pandas.core.frame.DataFrame'>
Index: 1058 entries, 1 to 1469
Data columns (total 32 columns):
 #   Column                    Non-Null Count  Dtype   
---  ------                    --------------  -----   
 0   EmployeeId                1058 non-null   object  
 1   Age                       1058 non-null   int64   
 2   Attrition                 1058 non-null   int32   
 3   BusinessTravel            1058 non-null   category
 4   DailyRate                 1058 non-null   int64   
 5   Department                1058 non-null   category
 6   DistanceFromHome          1058 non-null   int64   
 7   Education                 1058 non-null   object  
 8   EducationField            1058 non-null   category
 9   EnvironmentSatisfaction   1058 non-null   object  
 10  Gender                    1058 non-null   category
 11  HourlyRate                1058 non-null   int64   
 12  JobInvolvement            1058 non-null   object  
 13  JobLevel                  1058 non-null   object  
 14  JobRole                   1058 non-null   category
 15  JobSatisfaction           1058 non-null   object  
 16  MaritalStatus             1058 non-null   category
 17  MonthlyIncome             1058 non-null   int64   
 18  MonthlyRate               1058 non-null   int64   
 19  NumCompaniesWorked        1058 non-null   int64   
 20  OverTime                  1058 non-null   category
 21  PercentSalaryHike         1058 non-null   int64   
 22  PerformanceRating         1058 non-null   object  
 23  RelationshipSatisfaction  1058 non-null   object  
 24  StockOptionLevel          1058 non-null   object  
 25  TotalWorkingYears         1058 non-null   int64   
 26  TrainingTimesLastYear     1058 non-null   int64   
 27  WorkLifeBalance           1058 non-null   object  
 28  YearsAtCompany            1058 non-null   int64   
 29  YearsInCurrentRole        1058 non-null   int64   
 30  YearsSinceLastPromotion   1058 non-null   int64   
 31  YearsWithCurrManager      1058 non-null   int64   
dtypes: category(7), int32(1), int64(14), object(10)
memory usage: 251.5+ KB

# Descriptive statistics
print("\nDescriptive statistics of numeric category columns:")
display(employee_df[numeric_cols].describe())

# Descriptive statistics for the category column
print("\nDescriptive statistics for the category column:")
display(employee_df[categorical_cols].describe(include='all'))

Descriptive statistics of numeric category columns:

Descriptive statistics for the category column:

from scripts.runPlot_ObsHistNumeric import plot_obs_histnums
plot_obs_histnums(df=employee_plot, numeric_cols=numeric_cols, n_cols=4, color='salmon')

from scripts.runPlot_ObsHistCatgs import plot_obs_histcatgs
plot_obs_histcatgs(df=employee_plot, categorical_cols=categorical_cols, n_cols=4)

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Creating a scatter matrix (pair plot)
g = sns.pairplot(employee_plot, vars=numeric_cols)
# Ganti ukuran figure secara manual
g.fig.set_size_inches(20, 20)  # (lebar, tinggi)
plt.show()

from scripts.runPlot_ObsCorrHeatmap import plot_obs_corrheatmap
plot_obs_corrheatmap(employee_plot, columns=numeric_cols,figsize=(18, 10))

from scripts.runPlot_ObsBoxCols import plot_obs_boxcols
plot_obs_boxcols(employee_plot, numeric_cols=numeric_cols, col_target='Attrition', label_map=label_map, n_cols=4)

from scripts.runPlot_ObsCountCols import plot_obs_countcols
plot_obs_countcols(df=employee_plot, categorical_cols=categorical_cols, col_target='Attrition', n_cols=4,legend_show=False,label_map=label_map)

from scripts.runPlot_ObsLineXYcMetricSubplot import plot_obs_lineXYc_metric_subplot
from scripts.runPlot_ObsHitsXYcMetricSubplot import plot_obs_histXYc_metric_subplot


XcolsList = [col for col in categorical_cols if col != 'Attrition'] + [col for col in numeric_cols if col != 'Attrition']
XcolsList.sort()
print("columns List to plot:")
print(XcolsList)

columns List to plot:
['Age', 'BusinessTravel', 'DailyRate', 'Department', 'DistanceFromHome', 'Education', 'EducationField', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate', 'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction', 'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked', 'OverTime', 'PercentSalaryHike', 'PerformanceRating', 'RelationshipSatisfaction', 'StockOptionLevel', 'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance', 'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion', 'YearsWithCurrManager']

YCol = "Attrition"
Xline_smooth =  True
rotation_label = 90
metric_types = ["count","nimax","rank","catg_contrib"]
continue_cols=["Age", "HourlyRate","DailyRate", "MonthlyIncome","MonthlyRate"]
metric_types_map = {
    "count": "Count Plot",
    "nimax": "niMax Normalization Plot",
    "rank": "Rangking Metric Plot",
    "catg_contrib": "Percentage Contribution Metric Plot"
}


for metric_type in metric_types:
    print(f"------------ Metric Plot: {metric_types_map[metric_type]} ------------")

    plot_obs_lineXYc_metric_subplot(
        employee_plot,
        Xcols=XcolsList,
        lineCol=YCol,
        n_cols=4,
        ignore_cols=None,
        label_map=label_map,
        line_smooth=Xline_smooth,
        rotation_label=rotation_label,
        height_per_plot=5,
        legend_show=True,
        metric_type=metric_type)

    plot_obs_histXYc_metric_subplot(
        employee_plot,
        Xcols=continue_cols,
        barCol=YCol,
        metric_type=metric_type,
        label_map=label_map,
        n_cols=2,
        width_per_plot=18,
        height_per_plot=4,
        rotation_label=rotation_label,
        bins=30,
        legend_show=False)

------------ Metric Plot: Count Plot ------------

------------ Metric Plot: niMax Normalization Plot ------------

------------ Metric Plot: Rangking Metric Plot ------------

------------ Metric Plot: Percentage Contribution Metric Plot ------------

employee_df_clean.to_parquet("saved/employee_cleaned.parquet", index=False)
employee_plot.to_csv("saved/employee_plot.csv", index=False)

from sklearn.preprocessing import LabelEncoder

print(categorical_cols)

employee_df_encode = employee_df_clean.copy()

le = LabelEncoder()
for col in categorical_cols:
    employee_df_encode[col] = le.fit_transform(employee_df_encode[col])

Index(['BusinessTravel', 'Department', 'Education', 'EducationField',
       'EnvironmentSatisfaction', 'Gender', 'JobInvolvement', 'JobLevel',
       'JobRole', 'JobSatisfaction', 'MaritalStatus', 'OverTime',
       'PerformanceRating', 'RelationshipSatisfaction', 'StockOptionLevel',
       'WorkLifeBalance'],
      dtype='object')

X = employee_df_encode.drop(columns=['EmployeeId','Attrition'])
y = employee_df_encode['Attrition']

from scripts.runModelSuprt import *

resultRunModel_df = newDf_logModel()
models_path = "__models__/"

# import script runModel
from scripts.runModel_LogRegression import *

# running several model trainings
#------------------
setModelRun = {
    "IdRun": "LR01",
    "options": {"scheme":20},
    "Params": {"modelParams":{"random_state": 24}}
}

# Simulation model, runModel_LogRegression
setModel_Output, model_output = runModel_LogRegression(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "Logistic Regression")


#------------------
setModelRun = {
    "IdRun": "LR02",
    "options": {"scheme":30},
    "Params": {"modelParams":{"random_state": 24}}
}

# Simulation model, runModel_LogRegression
setModel_Output, model_output = runModel_LogRegression(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "Logistic Regression")


#------------------
setModelRun = {
    "IdRun": "LR03",
    "options": {"scheme":30,"gridSCV": True},
    "Params": {"modelParams":{"random_state": 24}}
}

# Simulation model, runModel_LogRegression
setModel_Output, model_output = runModel_LogRegression(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "Logistic Regression")


#------------------
setModelRun = {
    "IdRun": "LR04",
    "options": {"scheme":30,"randSCV": True},
    "Params": {"modelParams":{"random_state": 24}}
}

# Simulation model, runModel_LogRegression
setModel_Output, model_output = runModel_LogRegression(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "Logistic Regression")

# import script runModel
from scripts.runModel_RandomForest import *

# Running multiple models
#------------------

setModelRun = {
    "IdRun": "RF01",
    "options": {"scheme":20},
    "Params": {"modelParams":{"random_state": 24}}
}

# Model simulation, runModel_RandomForest
setModel_Output, model_output = runModel_RandomForest(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "Random Forest")


#------------------
setModelRun = {
    "IdRun": "RF02",
    "options": {"scheme":30},
    "Params": {"modelParams":{"random_state": 24}}
}

# Model simulation, runModel_RandomForest
setModel_Output, model_output = runModel_RandomForest(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "Random Forest")


#------------------
setModelRun = {
    "IdRun": "RF03",
    "options": {"scheme":30,"gridSCV": True},
}

# Model simulation, runModel_RandomForest
setModel_Output, model_output = runModel_RandomForest(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "Random Forest")


#------------------
setModelRun = {
    "IdRun": "RF04",
    "options": {"scheme":30,"randSCV": True},
}

# Model simulation, runModel_RandomForest
setModel_Output, model_output = runModel_RandomForest(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "Random Forest")

# import script runModel
from scripts.runModel_DecisionTree import *

# Running several training models
#------------------
setModelRun = {
    "IdRun": "DF01",
    "options": {"scheme":20},
}

# Simulation model, runModel_DecisionTree
setModel_Output, model_output = runModel_DecisionTree(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "DecisionTree Classifier")


#------------------
setModelRun = {
    "IdRun": "DF02",
    "options": {"scheme":30},
}

# Simulation model, runModel_DecisionTree
setModel_Output, model_output = runModel_DecisionTree(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "DecisionTree Classifier")


#------------------
setModelRun = {
    "IdRun": "DF03",
    "options": {"scheme":30,"gridSCV": True},
}

# Simulation model, runModel_DecisionTree
setModel_Output, model_output = runModel_DecisionTree(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "DecisionTree Classifier")


#------------------
setModelRun = {
    "IdRun": "DF04",
    "options": {"scheme":30,"randSCV": True},
}

# Simulation model, runModel_DecisionTree
setModel_Output, model_output = runModel_DecisionTree(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "DecisionTree Classifier")

from scripts.runModel_Adaboost import *


# Running several training models
#------------------
setModelRun = {
    "IdRun": "AD01",
    "options": {"scheme":20},
}

# Model simulation, runModel_Adaboost
setModel_Output, model_output = runModel_Adaboost(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "Adaboost Classifier")


#------------------
setModelRun = {
    "IdRun": "AD02",
    "options": {"scheme":30},
}

# Model simulation, runModel_Adaboost
setModel_Output, model_output = runModel_Adaboost(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "Adaboost Classifier")


#------------------
setModelRun = {
    "IdRun": "AD03",
    "options": {"scheme":30,"gridSCV": True},
}

# Model simulation, runModel_Adaboost
setModel_Output, model_output = runModel_Adaboost(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "Adaboost Classifier")


#------------------
setModelRun = {
    "IdRun": "AD04",
    "options": {"scheme":30,"randSCV": True},
}

# Model simulation, runModel_Adaboost
setModel_Output, model_output = runModel_Adaboost(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "Adaboost Classifier")

from scripts.runModel_GradientBoosting import *

# Running several training models
#------------------
setModelRun = {
    "IdRun": "GB01",
    "options": {"scheme":20},
}

# Model simulation, runModel_GradientBoosting
setModel_Output, model_output = runModel_GradientBoosting(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "Gradient Boosting")


#------------------
setModelRun = {
    "IdRun": "GB02",
    "options": {"scheme":30},
}

# Model simulation, runModel_GradientBoosting
setModel_Output, model_output = runModel_GradientBoosting(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "Gradient Boosting")


#------------------
setModelRun = {
    "IdRun": "GB03",
    "options": {"scheme":30,"gridSCV": True},
}

# Model simulation, runModel_GradientBoosting
setModel_Output, model_output = runModel_GradientBoosting(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "Gradient Boosting")


#------------------
setModelRun = {
    "IdRun": "GB04",
    "options": {"scheme":30,"randSCV": True},
}

# Model simulation, runModel_GradientBoosting
setModel_Output, model_output = runModel_GradientBoosting(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "Gradient Boosting")

from scripts.runModel_SVM import *

# Running several training models
#------------------
setModelRun = {
    "IdRun": "SV01",
    "options": {"scheme":20},
}

# Model simulation, runModel_SVM
setModel_Output, model_output = runModel_SVM(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "Support Vector Machine")


#------------------
setModelRun = {
    "IdRun": "SV02",
    "options": {"scheme":30},
}

# Model simulation, runModel_SVM
setModel_Output, model_output = runModel_SVM(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "Support Vector Machine")

from scripts.runModel_KNN import *


# Running several training models
#------------------
setModelRun = {
    "IdRun": "KN01",
    "options": {"scheme":20},
}

# Model simulation, runModel_KNN
setModel_Output, model_output = runModel_KNN(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "K-Nearest Neighbors")


#------------------
setModelRun = {
    "IdRun": "KN02",
    "options": {"scheme":30},
}

# Model simulation, runModel_KNN
setModel_Output, model_output = runModel_KNN(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "K-Nearest Neighbors")


#------------------
setModelRun = {
    "IdRun": "KN03",
    "options": {"scheme":30,"gridSCV": True},
}

# Model simulation, runModel_KNN
setModel_Output, model_output = runModel_KNN(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "K-Nearest Neighbors")


#------------------
setModelRun = {
    "IdRun": "KN04",
    "options": {"scheme":30,"randSCV": True},
}

# Model simulation, runModel_KNN
setModel_Output, model_output = runModel_KNN(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "K-Nearest Neighbors")

from scripts.runModel_XGBoost import *

# Running several training models
#------------------
setModelRun = {
    "IdRun": "XG01",
    "options": {"scheme":20},
}

# Model simulation, runModel_XGBoost
setModel_Output, model_output = runModel_XGBoost(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "XGBoost")


#------------------
setModelRun = {
    "IdRun": "XG02",
    "options": {"scheme":30},
}

# Model simulation, runModel_XGBoost
setModel_Output, model_output = runModel_XGBoost(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "XGBoost")


#------------------
setModelRun = {
    "IdRun": "XG03",
    "options": {"scheme":30,"gridSCV": True},
}

# Model simulation, runModel_XGBoost
setModel_Output, model_output = runModel_XGBoost(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "XGBoost")


#------------------
setModelRun = {
    "IdRun": "XG04",
    "options": {"scheme":30,"randSCV": True},
}

# Model simulation, runModel_XGBoost
setModel_Output, model_output = runModel_XGBoost(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "XGBoost")

from scripts.runModel_MLP import *


# Running several training models
#------------------
setModelRun = {
    "IdRun": "MP01",
    "options": {"scheme":20},
}

# Model simulation, runModel_MLP
setModel_Output, model_output = runModel_MLP(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "Multi-Layer Perceptron")


#------------------
setModelRun = {
    "IdRun": "MP02",
    "options": {"scheme":30},
}

# Model simulation, runModel_MLP
setModel_Output, model_output = runModel_MLP(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "Multi-Layer Perceptron")


#------------------
setModelRun = {
    "IdRun": "MP03",
    "options": {"scheme":30,"gridSCV": True},
}

# Model simulation, runModel_MLP
setModel_Output, model_output = runModel_MLP(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "Multi-Layer Perceptron")


#------------------
setModelRun = {
    "IdRun": "MP04",
    "options": {"scheme":30,"randSCV": True},
}

# Model simulation, runModel_MLP
setModel_Output, model_output = runModel_MLP(setModelRun, X, y)

# Set the Log dataframe and save the model
resultRunModel_df = updateRunModelDf(df=resultRunModel_df, setx=setModel_Output, model=model_output, models_path=models_path)
showResultDfModel(resultRunModel_df, ModelName = "Multi-Layer Perceptron")

resultRunModel_df

from scripts.getCalcRank_RModels import get_calc_rank_rmodels
df_model_rankscore = pd.read_csv(f"{models_path}resultRunModel_df.csv", index_col=0)
df_model_rankscore = get_calc_rank_rmodels(df_model_rankscore)
df_model_rankscore

# =========================Import module and setup=========================
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from joblib import load
from prettytable import PrettyTable

# Configure the display of pandas DataFrame to be maximized during debugging
pd.set_option("display.max_columns", None)
pd.set_option("display.max_colwidth", None)
pd.set_option("display.width", 0)
pd.set_option("display.expand_frame_repr", False)

# =========================1. Load Dataset & Model=========================
employee_file = 'saved/employee_attrition_nan.csv'
employee_df = pd.read_csv(employee_file)

model_file = "models/model_GB04.pkl"
loaded_model = load(model_file)

# =========================2. Column Data Type=========================
employee_col_numeric_int = pd.Index([
    'Age', 'DailyRate', 'DistanceFromHome', 'HourlyRate',
    'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',
    'PercentSalaryHike', 'TotalWorkingYears', 'TrainingTimesLastYear',
    'YearsAtCompany', 'YearsInCurrentRole',
    'YearsSinceLastPromotion', 'YearsWithCurrManager'
])

employee_col_categorical = pd.Index([
    'BusinessTravel', 'Department', 'Education', 'EducationField',
    'EnvironmentSatisfaction', 'Gender', 'JobInvolvement', 'JobLevel',
    'JobRole', 'JobSatisfaction', 'MaritalStatus', 'OverTime',
    'PerformanceRating', 'RelationshipSatisfaction', 'StockOptionLevel',
    'WorkLifeBalance'
])

employee_col_object = pd.Index(['EmployeeId'])

# ======================== 3. Data Type Conversion =========================
employee_df[employee_col_numeric_int] = employee_df[employee_col_numeric_int].astype('int64')
employee_df[employee_col_categorical] = employee_df[employee_col_categorical].astype('category')
employee_df['EmployeeId'] = employee_df['EmployeeId'].astype('object')

# =========================4. Encoding Categorical Column=========================
categorical_cols = employee_df.select_dtypes(include=['object', 'category', 'bool']).columns.tolist()
employee_df_encode = employee_df.copy()

le = LabelEncoder()
for col in categorical_cols:
    employee_df_encode[col] = le.fit_transform(employee_df_encode[col])

# =========================5. Predictions & Results=========================
employee_id_list = employee_df_encode['EmployeeId'].tolist()
results = []

for employee_id in employee_id_list:
    row_prediksi = employee_df_encode[employee_df_encode['EmployeeId'] == employee_id]
    row_prediksi = row_prediksi.drop(columns=['EmployeeId', 'Attrition'])
    
    prediction = loaded_model.predict(row_prediksi)[0]
    prediction_label = "Active" if prediction == 0 else "Resigned"
    
    results.append({
        'EmployeeId': employee_id,
        'Prediction': prediction,
        'Status': prediction_label
    })

df_predictions = pd.DataFrame(results)

# Save Results to CSV
output_file = os.path.join('saved', employee_file.replace('.csv', '') + '_predictions.csv')
output_file = employee_file.replace('.csv', '') + '_predictions.csv'
df_predictions.to_csv(output_file, index=False)


# ========================= 7. Print Results with PrettyTable =========================
table = PrettyTable()
table.field_names = df_predictions.columns.tolist()

for _, row in df_predictions.iterrows():
    table.add_row(row.tolist())

print(table)

+------------+------------+----------+
| EmployeeId | Prediction |  Status  |
+------------+------------+----------+
|     0      |     0      |  Active  |
|     1      |     0      |  Active  |
|     2      |     0      |  Active  |
|     3      |     0      |  Active  |
|     4      |     0      |  Active  |
|     5      |     0      |  Active  |
|     6      |     0      |  Active  |
|     7      |     0      |  Active  |
|     8      |     0      |  Active  |
|     9      |     0      |  Active  |
|     10     |     0      |  Active  |
|     11     |     0      |  Active  |
|     12     |     0      |  Active  |
|     13     |     0      |  Active  |
|     14     |     0      |  Active  |
|     15     |     0      |  Active  |
|     16     |     0      |  Active  |
|     17     |     0      |  Active  |
|     18     |     0      |  Active  |
|     19     |     0      |  Active  |
|     20     |     0      |  Active  |
|     21     |     0      |  Active  |
|     22     |     0      |  Active  |
|     23     |     0      |  Active  |
|     24     |     0      |  Active  |
|     25     |     0      |  Active  |
|     26     |     0      |  Active  |
|     27     |     0      |  Active  |
|     28     |     0      |  Active  |
|     29     |     1      | Resigned |
|     30     |     0      |  Active  |
|     31     |     1      | Resigned |
|     32     |     0      |  Active  |
|     33     |     0      |  Active  |
|     34     |     0      |  Active  |
|     35     |     0      |  Active  |
|     36     |     0      |  Active  |
|     37     |     0      |  Active  |
|     38     |     1      | Resigned |
|     39     |     0      |  Active  |
|     40     |     0      |  Active  |
|     41     |     0      |  Active  |
|     42     |     0      |  Active  |
|     43     |     0      |  Active  |
|     44     |     0      |  Active  |
|     45     |     0      |  Active  |
|     46     |     0      |  Active  |
|     47     |     0      |  Active  |
|     48     |     0      |  Active  |
|     49     |     0      |  Active  |
|     50     |     0      |  Active  |
|     51     |     0      |  Active  |
|     52     |     0      |  Active  |
|     53     |     0      |  Active  |
|     54     |     0      |  Active  |
|     55     |     0      |  Active  |
|     56     |     0      |  Active  |
|     57     |     0      |  Active  |
|     58     |     0      |  Active  |
|     59     |     1      | Resigned |
|     60     |     0      |  Active  |
|     61     |     0      |  Active  |
|     62     |     0      |  Active  |
|     63     |     0      |  Active  |
|     64     |     0      |  Active  |
|     65     |     0      |  Active  |
|     66     |     0      |  Active  |
|     67     |     0      |  Active  |
|     68     |     0      |  Active  |
|     69     |     0      |  Active  |
|     70     |     1      | Resigned |
|     71     |     0      |  Active  |
|     72     |     0      |  Active  |
|     73     |     0      |  Active  |
|     74     |     0      |  Active  |
|     75     |     0      |  Active  |
|     76     |     0      |  Active  |
|     77     |     0      |  Active  |
|     78     |     0      |  Active  |
|     79     |     0      |  Active  |
|     80     |     0      |  Active  |
|     81     |     0      |  Active  |
|     82     |     0      |  Active  |
|     83     |     0      |  Active  |
|     84     |     0      |  Active  |
|     85     |     0      |  Active  |
|     86     |     0      |  Active  |
|     87     |     0      |  Active  |
|     88     |     1      | Resigned |
|     89     |     0      |  Active  |
|     90     |     0      |  Active  |
|     91     |     0      |  Active  |
|     92     |     0      |  Active  |
|     93     |     0      |  Active  |
|     94     |     0      |  Active  |
|     95     |     0      |  Active  |
|     96     |     0      |  Active  |
|     97     |     0      |  Active  |
|     98     |     0      |  Active  |
|     99     |     0      |  Active  |
|    100     |     0      |  Active  |
|    101     |     0      |  Active  |
|    102     |     0      |  Active  |
|    103     |     0      |  Active  |
|    104     |     0      |  Active  |
|    105     |     0      |  Active  |
|    106     |     0      |  Active  |
|    107     |     0      |  Active  |
|    108     |     0      |  Active  |
|    109     |     0      |  Active  |
|    110     |     0      |  Active  |
|    111     |     0      |  Active  |
|    112     |     0      |  Active  |
|    113     |     0      |  Active  |
|    114     |     0      |  Active  |
|    115     |     0      |  Active  |
|    116     |     0      |  Active  |
|    117     |     0      |  Active  |
|    118     |     0      |  Active  |
|    119     |     0      |  Active  |
|    120     |     0      |  Active  |
|    121     |     1      | Resigned |
|    122     |     0      |  Active  |
|    123     |     0      |  Active  |
|    124     |     0      |  Active  |
|    125     |     0      |  Active  |
|    126     |     0      |  Active  |
|    127     |     0      |  Active  |
|    128     |     0      |  Active  |
|    129     |     0      |  Active  |
|    130     |     0      |  Active  |
|    131     |     0      |  Active  |
|    132     |     0      |  Active  |
|    133     |     0      |  Active  |
|    134     |     1      | Resigned |
|    135     |     0      |  Active  |
|    136     |     1      | Resigned |
|    137     |     0      |  Active  |
|    138     |     0      |  Active  |
|    139     |     0      |  Active  |
|    140     |     0      |  Active  |
|    141     |     0      |  Active  |
|    142     |     0      |  Active  |
|    143     |     0      |  Active  |
|    144     |     0      |  Active  |
|    145     |     0      |  Active  |
|    146     |     0      |  Active  |
|    147     |     0      |  Active  |
|    148     |     1      | Resigned |
|    149     |     0      |  Active  |
|    150     |     0      |  Active  |
|    151     |     0      |  Active  |
|    152     |     0      |  Active  |
|    153     |     0      |  Active  |
|    154     |     0      |  Active  |
|    155     |     0      |  Active  |
|    156     |     0      |  Active  |
|    157     |     0      |  Active  |
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|    165     |     1      | Resigned |
|    166     |     0      |  Active  |
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|    168     |     0      |  Active  |
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|    191     |     0      |  Active  |
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|    197     |     0      |  Active  |
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|    208     |     0      |  Active  |
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|    232     |     0      |  Active  |
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|    250     |     1      | Resigned |
|    251     |     0      |  Active  |
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|    257     |     0      |  Active  |
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|    260     |     1      | Resigned |
|    261     |     0      |  Active  |
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|    267     |     1      | Resigned |
|    268     |     0      |  Active  |
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|    304     |     1      | Resigned |
|    305     |     0      |  Active  |
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|    309     |     1      | Resigned |
|    310     |     0      |  Active  |
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|    318     |     1      | Resigned |
|    319     |     0      |  Active  |
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|    332     |     1      | Resigned |
|    333     |     0      |  Active  |
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|    336     |     0      |  Active  |
|    337     |     0      |  Active  |
|    338     |     1      | Resigned |
|    339     |     0      |  Active  |
|    340     |     0      |  Active  |
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|    343     |     1      | Resigned |
|    344     |     0      |  Active  |
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|    349     |     0      |  Active  |
|    350     |     0      |  Active  |
|    351     |     1      | Resigned |
|    352     |     0      |  Active  |
|    353     |     1      | Resigned |
|    354     |     1      | Resigned |
|    355     |     0      |  Active  |
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|    361     |     1      | Resigned |
|    362     |     0      |  Active  |
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|    370     |     0      |  Active  |
|    371     |     1      | Resigned |
|    372     |     0      |  Active  |
|    373     |     0      |  Active  |
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|    375     |     0      |  Active  |
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|    377     |     1      | Resigned |
|    378     |     0      |  Active  |
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|    382     |     0      |  Active  |
|    383     |     1      | Resigned |
|    384     |     0      |  Active  |
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|    405     |     0      |  Active  |
|    406     |     0      |  Active  |
|    407     |     0      |  Active  |
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|    411     |     0      |  Active  |
+------------+------------+----------+

	Age	Attrition	DailyRate	DistanceFromHome	HourlyRate	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	TotalWorkingYears	TrainingTimesLastYear	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
count	1470.000000	1058.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000
mean	36.923810	0.169187	802.485714	9.192517	65.891156	6502.931293	14313.103401	2.693197	15.209524	11.279592	2.799320	7.008163	4.229252	2.187755	4.123129
std	9.135373	0.375094	403.509100	8.106864	20.329428	4707.956783	7117.786044	2.498009	3.659938	7.780782	1.289271	6.126525	3.623137	3.222430	3.568136
min	18.000000	0.000000	102.000000	1.000000	30.000000	1009.000000	2094.000000	0.000000	11.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	30.000000	0.000000	465.000000	2.000000	48.000000	2911.000000	8047.000000	1.000000	12.000000	6.000000	2.000000	3.000000	2.000000	0.000000	2.000000
50%	36.000000	0.000000	802.000000	7.000000	66.000000	4919.000000	14235.500000	2.000000	14.000000	10.000000	3.000000	5.000000	3.000000	1.000000	3.000000
75%	43.000000	0.000000	1157.000000	14.000000	83.750000	8379.000000	20461.500000	4.000000	18.000000	15.000000	3.000000	9.000000	7.000000	3.000000	7.000000
max	60.000000	1.000000	1499.000000	29.000000	100.000000	19999.000000	26999.000000	9.000000	25.000000	40.000000	6.000000	40.000000	18.000000	15.000000	17.000000

	ModelName	IdRun	DateAction	options	Params	Remarks	AccuracyTrain	AccuracyTest	F1ScoreTest	PrecisionTest	RecallTest
0	Logistic Regression	LR01	2025-05-08 03:01:02	{'scheme': 20}	{'modelParams': {'random_state': 24}}	NaN	0.836879	0.820755	0.744482	0.853036	0.820755
1	Logistic Regression	LR02	2025-05-08 03:01:02	{'scheme': 30}	{'modelParams': {'random_state': 24}}	NaN	0.833784	0.836478	0.770531	0.863472	0.836478
2	Logistic Regression	LR03	2025-05-08 03:01:07	{'scheme': 30, 'gridSCV': True}	{'modelParams': {'random_state': 24, 'n_jobs': 2}, 'BestParamsGridSCV': {'C': 0.1, 'penalty': 'l2', 'solver': 'liblinear'}}	NaN	0.862162	0.880503	0.852995	0.895589	0.880503
3	Logistic Regression	LR04	2025-05-08 03:01:10	{'scheme': 30, 'randSCV': True}	{'modelParams': {'max_iter': 500, 'random_state': 24}, 'BestParamsRandSCV': {'C': 0.13292918943162169, 'penalty': 'l1', 'solver': 'liblinear'}}	NaN	0.855405	0.874214	0.845258	0.882668	0.874214

	ModelName	IdRun	DateAction	options	Params	Remarks	AccuracyTrain	AccuracyTest	F1ScoreTest	PrecisionTest	RecallTest
4	Random Forest	RF01	2025-05-08 03:01:11	{'scheme': 20}	{'modelParams': {'n_jobs': 2, 'random_state': 24}}	NaN	1.000000	0.849057	0.802944	0.872618	0.849057
5	Random Forest	RF02	2025-05-08 03:01:12	{'scheme': 30}	{'modelParams': {'n_jobs': 2, 'random_state': 24}}	NaN	1.000000	0.861635	0.820854	0.881466	0.861635
6	Random Forest	RF03	2025-05-08 03:06:12	{'scheme': 30, 'gridSCV': True}	{'modelParams': {'random_state': 42, 'n_jobs': 2}, 'BestParamsGridSCV': {'max_depth': 20, 'max_features': 0.5, 'min_samples_leaf': 1, 'min_samples_split': 15, 'n_estimators': 500}}	NaN	0.935135	0.855346	0.812711	0.863049	0.855346
7	Random Forest	RF04	2025-05-08 03:06:18	{'scheme': 30, 'randSCV': True}	{'modelParams': {'random_state': 42, 'n_jobs': 2}, 'BestParamsRandSCV': {'n_estimators': 50, 'min_samples_split': 2, 'max_depth': 10}}	NaN	0.993243	0.849057	0.800881	0.854986	0.849057

	ModelName	IdRun	DateAction	options	Params	Remarks	AccuracyTrain	AccuracyTest	F1ScoreTest	PrecisionTest	RecallTest
0	DecisionTree Classifier	DF01	2025-05-08 03:06:18	{'scheme': 20}	{'modelParams': {'random_state': 42}}	NaN	1.000000	0.764151	0.750122	0.739353	0.764151
1	DecisionTree Classifier	DF02	2025-05-08 03:06:18	{'scheme': 30}	{'modelParams': {'random_state': 42}}	NaN	1.000000	0.783019	0.783790	0.784576	0.783019
2	DecisionTree Classifier	DF03	2025-05-08 03:06:23	{'scheme': 30, 'gridSCV': True}	{'modelParams': {'random_state': 42}, 'BestParamsGridSCV': {'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 2}}	NaN	0.964865	0.811321	0.793812	0.784271	0.811321
3	DecisionTree Classifier	DF04	2025-05-08 03:06:23	{'scheme': 30, 'randSCV': True}	{'modelParams': {'random_state': 42}, 'BestParamsRandSCV': {'criterion': 'entropy', 'max_depth': 2, 'min_samples_leaf': 8, 'min_samples_split': 13}}	NaN	0.836486	0.858491	0.838929	0.841739	0.858491

	ModelName	IdRun	DateAction	options	Params	Remarks	AccuracyTrain	AccuracyTest	F1ScoreTest	PrecisionTest	RecallTest
0	Adaboost Classifier	AD01	2025-05-08 03:06:24	{'scheme': 20}	{'modelParams': {'random_state': 42}}	NaN	0.875887	0.853774	0.834035	0.838556	0.853774
1	Adaboost Classifier	AD02	2025-05-08 03:06:24	{'scheme': 30}	{'modelParams': {'random_state': 42}}	NaN	0.870270	0.867925	0.840028	0.864816	0.867925
2	Adaboost Classifier	AD03	2025-05-08 03:06:39	{'scheme': 30, 'gridSCV': True}	{'modelParams': {'random_state': 42}, 'BestParamsGridSCV': {'learning_rate': 1.0, 'n_estimators': 50}}	NaN	0.870270	0.867925	0.840028	0.864816	0.867925
3	Adaboost Classifier	AD04	2025-05-08 03:06:50	{'scheme': 30, 'randSCV': True}	{'modelParams': {'random_state': 42}, 'BestParamsRandSCV': {'learning_rate': 0.7319987722668247, 'n_estimators': 79}}	NaN	0.871622	0.874214	0.845258	0.882668	0.874214

	EmployeeId	Age	Attrition	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EnvironmentSatisfaction	Gender	HourlyRate	JobInvolvement	JobLevel	JobRole	JobSatisfaction	MaritalStatus	MonthlyIncome	MonthlyRate	NumCompaniesWorked	Over18	OverTime	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
326	327	45	1.0	Travel_Frequently	306	Sales	26	4	Life Sciences	1	1	Female	100	3	2	Sales Executive	1	Married	4286	5630	2	Y	No	14	3	4	80	2	5	4	3	1	1	0	0
566	567	26	0.0	Travel_Frequently	921	Research & Development	1	1	Medical	1	1	Female	66	2	1	Research Scientist	3	Divorced	2007	25265	1	Y	No	13	3	3	80	2	5	5	3	5	3	1	3
1164	1165	35	NaN	Travel_Rarely	1224	Sales	7	4	Life Sciences	1	3	Female	55	3	2	Sales Executive	4	Married	5204	13586	1	Y	Yes	11	3	4	80	0	10	2	3	10	8	0	9
156	157	56	NaN	Non-Travel	667	Research & Development	1	4	Life Sciences	1	3	Male	57	3	2	Healthcare Representative	3	Divorced	6306	26236	1	Y	No	21	4	1	80	1	13	2	2	13	12	1	9
346	347	26	0.0	Travel_Frequently	496	Research & Development	11	2	Medical	1	1	Male	60	3	2	Healthcare Representative	1	Married	4741	22722	1	Y	Yes	13	3	3	80	1	5	3	3	5	3	3	3
1134	1135	44	1.0	Travel_Rarely	621	Research & Development	15	3	Medical	1	1	Female	73	3	3	Healthcare Representative	4	Married	7978	14075	1	Y	No	11	3	4	80	1	10	2	3	10	7	0	5
456	457	27	0.0	Travel_Frequently	994	Sales	8	3	Life Sciences	1	4	Male	37	3	3	Sales Executive	3	Single	8726	2975	1	Y	No	15	3	4	80	0	9	0	3	9	8	1	7
526	527	36	1.0	Travel_Rarely	660	Research & Development	15	3	Other	1	1	Male	81	3	2	Laboratory Technician	3	Divorced	4834	7858	7	Y	No	14	3	2	80	1	9	3	2	1	0	0	0
1293	1294	43	NaN	Travel_Frequently	1422	Sales	2	4	Life Sciences	1	1	Male	92	3	2	Sales Executive	4	Married	5675	19246	1	Y	No	20	4	3	80	1	7	5	3	7	7	7	7
1230	1231	54	0.0	Travel_Rarely	397	Human Resources	19	4	Medical	1	3	Male	88	3	3	Human Resources	2	Married	10725	6729	2	Y	No	15	3	3	80	1	16	1	4	9	7	7	1

	0
EmployeeId	1470
Age	43
Attrition	2
BusinessTravel	3
DailyRate	886
Department	3
DistanceFromHome	29
Education	5
EducationField	6
EmployeeCount	1
EnvironmentSatisfaction	4
Gender	2
HourlyRate	71
JobInvolvement	4
JobLevel	5
JobRole	9
JobSatisfaction	4
MaritalStatus	3
MonthlyIncome	1349
MonthlyRate	1427
NumCompaniesWorked	10
Over18	1
OverTime	2
PercentSalaryHike	15
PerformanceRating	2
RelationshipSatisfaction	4
StandardHours	1
StockOptionLevel	4
TotalWorkingYears	40
TrainingTimesLastYear	7
WorkLifeBalance	4
YearsAtCompany	37
YearsInCurrentRole	19
YearsSinceLastPromotion	16
YearsWithCurrManager	18

	ModelName	IdRun	DateAction	options	Params	Remarks	AccuracyTrain	AccuracyTest	F1ScoreTest	PrecisionTest	RecallTest
8	Gradient Boosting	GB01	2025-05-08 03:06:51	{'scheme': 20}	{'modelParams': {'random_state': 42}}	NaN	0.971631	0.849057	0.816596	0.838627	0.849057
9	Gradient Boosting	GB02	2025-05-08 03:06:51	{'scheme': 30}	{'modelParams': {'random_state': 42}}	NaN	0.977027	0.852201	0.824893	0.833206	0.852201
10	Gradient Boosting	GB03	2025-05-08 03:08:31	{'scheme': 30, 'gridSCV': True}	{'modelParams': {'random_state': 42}, 'BestParamsGridSCV': {'learning_rate': 0.5, 'max_depth': 5, 'n_estimators': 50}}	NaN	1.000000	0.858491	0.840891	0.841839	0.858491
11	Gradient Boosting	GB04	2025-05-08 03:08:46	{'scheme': 30, 'randSCV': True}	{'modelParams': {'random_state': 42}, 'BestParamsRandSCV': {'learning_rate': 0.19727005942368125, 'max_depth': 3, 'n_estimators': 64}}	NaN	0.985135	0.871069	0.851357	0.860955	0.871069

	ModelName	IdRun	DateAction	options	Params	Remarks	AccuracyTrain	AccuracyTest	F1ScoreTest	PrecisionTest	RecallTest
20	Support Vector Machine	SV01	2025-05-08 03:08:47	{'scheme': 20}	{'modelParams': {'probability': True}}	NaN	0.834515	0.816038	0.733374	0.665918	0.816038
21	Support Vector Machine	SV02	2025-05-08 03:08:48	{'scheme': 30}	{'modelParams': {'probability': True}}	NaN	0.832432	0.827044	0.748752	0.684002	0.827044

	ModelName	IdRun	DateAction	options	Params	Remarks	AccuracyTrain	AccuracyTest	F1ScoreTest	PrecisionTest	RecallTest
12	K-Nearest Neighbors	KN01	2025-05-08 03:08:48	{'scheme': 20}	{'modelParams': {}}	NaN	0.855792	0.816038	0.774000	0.773373	0.816038
13	K-Nearest Neighbors	KN02	2025-05-08 03:08:49	{'scheme': 30}	{'modelParams': {}}	NaN	0.848649	0.814465	0.766051	0.752787	0.814465
14	K-Nearest Neighbors	KN03	2025-05-08 03:08:50	{'scheme': 30, 'gridSCV': True}	{'modelParams': {}, 'BestParamsGridSCV': {'metric': 'manhattan', 'n_neighbors': 9, 'weights': 'uniform'}}	NaN	0.841892	0.823899	0.747192	0.683551	0.823899
15	K-Nearest Neighbors	KN04	2025-05-08 03:08:51	{'scheme': 30, 'randSCV': True}	{'modelParams': {}, 'BestParamsRandSCV': {'metric': 'manhattan', 'n_neighbors': 14, 'weights': 'uniform'}}	NaN	0.832432	0.827044	0.748752	0.684002	0.827044

	ModelName	IdRun	DateAction	options	Params	Remarks	AccuracyTrain	AccuracyTest	F1ScoreTest	PrecisionTest	RecallTest
26	XGBoost	XG01	2025-05-08 03:08:52	{'scheme': 20}	{'modelParams': {'random_state': 42, 'use_label_encoder': False, 'eval_metric': 'mlogloss'}}	NaN	1.000000	0.839623	0.816353	0.818064	0.839623
27	XGBoost	XG02	2025-05-08 03:08:52	{'scheme': 30}	{'modelParams': {'random_state': 42, 'use_label_encoder': False, 'eval_metric': 'mlogloss'}}	NaN	1.000000	0.855346	0.836366	0.837337	0.855346
28	XGBoost	XG03	2025-05-08 03:09:52	{'scheme': 30, 'gridSCV': True}	{'modelParams': {'random_state': 42, 'use_label_encoder': False, 'eval_metric': 'mlogloss'}, 'BestParamsGridSCV': {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 50, 'subsample': 0.8}}	NaN	0.914865	0.874214	0.849896	0.872260	0.874214
29	XGBoost	XG04	2025-05-08 03:10:01	{'scheme': 30, 'randSCV': True}	{'modelParams': {'random_state': 42, 'use_label_encoder': False, 'eval_metric': 'mlogloss'}, 'BestParamsRandSCV': {'learning_rate': 0.29156581270472504, 'max_depth': 4, 'n_estimators': 70, 'subsample': 0.8852444528883149}}	NaN	1.000000	0.874214	0.855917	0.864935	0.874214

	ModelName	IdRun	DateAction	options	Params	Remarks	AccuracyTrain	AccuracyTest	F1ScoreTest	PrecisionTest	RecallTest
20	Multi-Layer Perceptron	MP01	2025-05-08 03:10:02	{'scheme': 20}	{'modelParams': {'max_iter': 300, 'random_state': 42}}	NaN	0.822695	0.811321	0.753680	0.751575	0.811321
21	Multi-Layer Perceptron	MP02	2025-05-08 03:10:02	{'scheme': 30}	{'modelParams': {'max_iter': 300, 'random_state': 42}}	NaN	0.831081	0.817610	0.744053	0.682640	0.817610
22	Multi-Layer Perceptron	MP03	2025-05-08 03:10:59	{'scheme': 30, 'gridSCV': True}	{'modelParams': {'max_iter': 300, 'random_state': 42}, 'BestParamsGridSCV': {'activation': 'tanh', 'alpha': 0.0001, 'hidden_layer_sizes': (100,), 'solver': 'adam'}}	NaN	0.832432	0.827044	0.748752	0.684002	0.827044
23	Multi-Layer Perceptron	MP04	2025-05-08 03:11:16	{'scheme': 30, 'randSCV': True}	{'modelParams': {'max_iter': 300, 'random_state': 42}, 'BestParamsRandSCV': {'activation': 'tanh', 'alpha': 0.006086584841970367, 'hidden_layer_sizes': (30, 30, 30), 'solver': 'sgd'}}	NaN	0.832432	0.827044	0.748752	0.684002	0.827044

Rank	Metric	Reason
1	F1ScoreTest	Balances precision and recall, most representative metric overall.
2	RecallTest	Important for maximizing true positives.
3	PrecisionTest	Important for high accuracy in positive predictions.
4	AccuracyTest	Commonly used, but biased with imbalanced datasets.
5	AccuracyTrain	Only useful for detecting overfitting, not for final model assessment.

Metric	Weight (0-1)	Explanation
F1ScoreTest	0.40	Main focus, balances both precision and recall.
RecallTest	0.20	Important to reduce false negatives.
PrecisionTest	0.15	Important to reduce false positives.
AccuracyTest	0.15	Still considered, but not the primary metric.
AccuracyTrain	0.10	Useful for overfitting detection, minor contribution.

Solving Human Resources Issues¶

Preparation¶

Source of data¶

Import the module and dataset¶

Data imported into dataframe 'employee_df'¶

EDA¶

Data Info¶

Data format¶

Zero-Variance Feature¶

Null, NaN¶

Outlier¶

Creating a new dataframe for data plotting needs¶

Descriptive Statistics "employee_df"¶

Plot data¶

Hitogram Plot¶

Pair plot¶

Correlation Headmap¶

Box Plot¶

Count Plot¶

Line plot¶

Saving the dataframe to a dataset file¶

Modeling¶

Encoding¶

Split feature data and target data¶

Building a Model¶

import def function to support the process.¶

Running the Model¶

Logistic Regression¶

Random Forest¶

Decision Tree¶

AdaBoost (Adaptive Boosting)¶

Gradient Boosting¶

Support Vector Machines (SVM)¶

K-Nearest Neighbors (KNN)¶

XGBoost (Extreme Gradient Boosting)¶

Multi-Layer Perceptron (MLP) – Neural Network¶

Evaluation¶

Rank Composite¶

Metric Ranking Based on General Perception in Machine Learning¶

Summary Ranking Based on General Perception:¶

Metric Weights (0-1):¶

Business Dashboard¶

Prediction (optional)¶